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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Objectives
Oracle AI and Machine Learning Services	- OCI AI Services 1. Speech 2. Vision 3. Document Understanding 4. Language - Oracle AI Stack 1. AI data and machine learning services 2. AI infrastructure - OCI Machine Learning Services 1. OCI Data Science and machine learning workflows 2. Machine learning capabilities in OCI
Generative AI and Large Language Models	- Large Language Models 1. LLM fundamentals 2. Language models and generative AI applications - Generative AI Fundamentals 1. Generative AI concepts and capabilities 2. Generative AI use cases
OCI Generative AI and Oracle Database AI Capabilities	- Oracle AI Database 1. Vector database concepts 2. AI capabilities in Oracle Database - OCI Generative AI 1. Generative AI models and applications 2. OCI Generative AI services - AI Application Frameworks 1. Retrieval and AI application concepts 2. Language frameworks
Artificial Intelligence and Machine Learning Fundamentals	- Deep Learning Fundamentals 1. Convolutional and sequence models 2. Neural networks - Machine Learning Fundamentals 1. Supervised and unsupervised learning 2. Machine learning models and architectures - Artificial Intelligence Concepts 1. AI use cases and applications 2. AI fundamentals and terminology

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q17-Q22):

NEW QUESTION # 17
How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?

A. Prompt Engineering adjusts the model ' s parameters, while Fine-tuning crafts input prompts.
B. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
C. Prompt Engineering modifies training data, while Fine-tuning alters the model ' s structure.
D. Both involve retraining the model, but Prompt Engineering does it more often.

Answer: B

Explanation:
In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
* Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model ' s internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
* Fine-tuning , on the other hand, refers to the process of retraining a pretrained model on a smaller, task- specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively " specializing " the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.

NEW QUESTION # 18
What would you use Oracle AI Vector Search for?

A. Store business data in a cloud database.
B. Query data based on keywords.
C. Query data based on semantics.
D. Manage database security protocols.

Answer: C

Explanation:
Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .

NEW QUESTION # 19
Emma is developing a customer support chatbot for an e-commerce website. The chatbot needs to provide accurate and up-to-date return policies, which change frequently. She initially tries fine-tuning but finds that the model still uses outdated information. Which approach should Emma use instead?

A. Prompt Engineering
B. Reinforcement Learning
C. Zero-shot Prompting
D. Retrieval-Augmented Generation (RAG)

Answer: D

Explanation:
Retrieval-Augmented Generation is the appropriate approach when a chatbot must answer using information that changes frequently. Oracle defines RAG as a technique that retrieves information from specific external data sources and augments an LLM ' s response with that retrieved context, producing grounded answers.
Oracle Docs Oracle further explains that RAG can incorporate information that is more current than the model
' s original training data and that knowledge repositories can be continually updated without retraining the underlying LLM. Oracle Docs Fine-tuning is better suited to adapting model behavior or specialization, not continuously changing factual information. Prompt engineering and zero-shot prompting control how instructions are presented but do not independently supply current return-policy data. Therefore, RAG is the correct solution for providing accurate, current, organization-specific policy responses.

NEW QUESTION # 20
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

A. It delivers exceptional performance and scalability for complex AI tasks.
B. It provides a cost-effective solution for simple AI tasks.
C. It is ideal for tasks such as text-to-speech conversion.
D. It offers seamless integration with social media platforms.

Answer: A

NEW QUESTION # 21
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

A. Explicability
B. Respect for human autonomy
C. Prevention of harm
D. Fairness

Answer: A

NEW QUESTION # 22
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